#source('http://bioconductor.org/biocLite.R')
#biocLite('phyloseq')
library(phyloseq)
library(ggplot2)
library(dplyr)
library(Rmisc)
setwd("~/Dropbox/clado-manuscript/Mikes_MS_Data/")
# Load biom file.
biom <- import_biom("OTU_table.biom", "~/Dropbox/clado-manuscript/Nephele/PipelineResults_NMEPINZ20QK1/nephele_outputs/tree.tre", parseFunction=parse_taxonomy_greengenes)
sam.data <- read.csv(file="sample.data.csv", row.names=1, header=TRUE)
head(sam.data)
## TreatmentGroup Site Date Description
## C172N1 Early North 172 Sample of day 172 at site North 1
## C172N2 Early North 172 Sample of day 172 at site North 2
## C172N3 Early North 172 Sample of day 172 at site North 3
## C172P1 Early Point 172 Sample of day 172 at site Point 1
## C172P2 Early Point 172 Sample of day 172 at site Point 2
## C172P3 Early Point 172 Sample of day 172 at site Point 3
## SampleID.1
## C172N1 C172N1
## C172N2 C172N2
## C172N3 C172N3
## C172P1 C172P1
## C172P2 C172P2
## C172P3 C172P3
sam.data$Date <- as.factor(sam.data$Date)
sam.data$DateSite <- paste(sam.data$Date, sam.data$Site)
head(sam.data); str(sam.data)
## TreatmentGroup Site Date Description
## C172N1 Early North 172 Sample of day 172 at site North 1
## C172N2 Early North 172 Sample of day 172 at site North 2
## C172N3 Early North 172 Sample of day 172 at site North 3
## C172P1 Early Point 172 Sample of day 172 at site Point 1
## C172P2 Early Point 172 Sample of day 172 at site Point 2
## C172P3 Early Point 172 Sample of day 172 at site Point 3
## SampleID.1 DateSite
## C172N1 C172N1 172 North
## C172N2 C172N2 172 North
## C172N3 C172N3 172 North
## C172P1 C172P1 172 Point
## C172P2 C172P2 172 Point
## C172P3 C172P3 172 Point
## 'data.frame': 52 obs. of 6 variables:
## $ TreatmentGroup: Factor w/ 2 levels "Early","Late": 1 1 1 1 1 1 1 1 1 1 ...
## $ Site : Factor w/ 3 levels "North","Point",..: 1 1 1 2 2 2 3 3 3 1 ...
## $ Date : Factor w/ 6 levels "172","178","185",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ Description : Factor w/ 52 levels "Sample of day 172 at site North 1",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ SampleID.1 : Factor w/ 52 levels "C172N1","C172N2",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ DateSite : chr "172 North" "172 North" "172 North" "172 Point" ...
sample_data(biom) <- sam.data
biom; sample_data(biom)
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 51928 taxa and 52 samples ]
## sample_data() Sample Data: [ 52 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 51928 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 51928 tips and 51914 internal nodes ]
## Sample Data: [52 samples by 6 sample variables]:
## TreatmentGroup Site Date Description
## C178N1 Early North 178 Sample of day 178 at site North 1
## C178P1 Early Point 178 Sample of day 178 at site Point 1
## C185P2 Early Point 185 Sample of day 185 at site Point 2
## C206N2 Late North 206 Sample of day 206 at site North 2
## C206P1 Late Point 206 Sample of day 206 at site Point 1
## C206P2 Late Point 206 Sample of day 206 at site Point 2
## C214P1 Late Point 214 Sample of day 214 at site Point 1
## C214P2 Late Point 214 Sample of day 214 at site Point 2
## C214P3 Late Point 214 Sample of day 214 at site Point 3
## C214S1 Late South 214 Sample of day 214 at site South 1
## C214S2 Late South 214 Sample of day 214 at site South 2
## C214S3 Late South 214 Sample of day 214 at site South 3
## C185P1 Early Point 185 Sample of day 185 at site Point 1
## C185P3 Early Point 185 Sample of day 185 at site Point 3
## C199P3 Late Point 199 Sample of day 199 at site Point 3
## C199S2 Late South 199 Sample of day 199 at site South 2
## C199S3 Late South 199 Sample of day 199 at site South 3
## C206N1 Late North 206 Sample of day 206 at site North 1
## C178P2 Early Point 178 Sample of day 178 at site Point 2
## C199N3 Late North 199 Sample of day 199 at site North 3
## C206S1 Late South 206 Sample of day 206 at site South 1
## C214N3 Late North 214 Sample of day 214 at site North 3
## C199N2 Late North 199 Sample of day 199 at site North 2
## C206N3 Late North 206 Sample of day 206 at site North 3
## C206S2 Late South 206 Sample of day 206 at site South 2
## C199N1 Late North 199 Sample of day 199 at site North 1
## C199P1 Late Point 199 Sample of day 199 at site Point 1
## C199S1 Late South 199 Sample of day 199 at site South 1
## C214N1 Late North 214 Sample of day 214 at site North 1
## C172P1 Early Point 172 Sample of day 172 at site Point 1
## C199P2 Late Point 199 Sample of day 199 at site Point 2
## C172N3 Early North 172 Sample of day 172 at site North 3
## C172S3 Early South 172 Sample of day 172 at site South 3
## C178S2 Early South 178 Sample of day 178 at site South 2
## C178P3 Early Point 178 Sample of day 178 at site Point 3
## C178S3 Early South 178 Sample of day 178 at site South 3
## C172N1 Early North 172 Sample of day 172 at site North 1
## C172S1 Early South 172 Sample of day 172 at site South 1
## C178N3 Early North 178 Sample of day 178 at site North 3
## C185N2 Early North 185 Sample of day 185 at site North 2
## C185N3 Early North 185 Sample of day 185 at site North 3
## C185S3 Early South 185 Sample of day 185 at site South 3
## C214N2 Late North 214 Sample of day 214 at site North 2
## C172P2 Early Point 172 Sample of day 172 at site Point 2
## C185S2 Early South 185 Sample of day 185 at site South 2
## C172P3 Early Point 172 Sample of day 172 at site Point 3
## C185N1 Early North 185 Sample of day 185 at site North 1
## C172N2 Early North 172 Sample of day 172 at site North 2
## C178S1 Early South 178 Sample of day 178 at site South 1
## C185S1 Early South 185 Sample of day 185 at site South 1
## C172S2 Early South 172 Sample of day 172 at site South 2
## C178N2 Early North 178 Sample of day 178 at site North 2
## SampleID.1 DateSite
## C178N1 C178N1 178 North
## C178P1 C178P1 178 Point
## C185P2 C185P2 185 Point
## C206N2 C206N2 206 North
## C206P1 C206P1 206 Point
## C206P2 C206P2 206 Point
## C214P1 C214P1 214 Point
## C214P2 C214P2 214 Point
## C214P3 C214P3 214 Point
## C214S1 C214S1 214 South
## C214S2 C214S2 214 South
## C214S3 C214S3 214 South
## C185P1 C185P1 185 Point
## C185P3 C185P3 185 Point
## C199P3 C199P3 199 Point
## C199S2 C199S2 199 South
## C199S3 C199S3 199 South
## C206N1 C206N1 206 North
## C178P2 C178P2 178 Point
## C199N3 C199N3 199 North
## C206S1 C206S1 206 South
## C214N3 C214N3 214 North
## C199N2 C199N2 199 North
## C206N3 C206N3 206 North
## C206S2 C206S2 206 South
## C199N1 C199N1 199 North
## C199P1 C199P1 199 Point
## C199S1 C199S1 199 South
## C214N1 C214N1 214 North
## C172P1 C172P1 172 Point
## C199P2 C199P2 199 Point
## C172N3 C172N3 172 North
## C172S3 C172S3 172 South
## C178S2 C178S2 178 South
## C178P3 C178P3 178 Point
## C178S3 C178S3 178 South
## C172N1 C172N1 172 North
## C172S1 C172S1 172 South
## C178N3 C178N3 178 North
## C185N2 C185N2 185 North
## C185N3 C185N3 185 North
## C185S3 C185S3 185 South
## C214N2 C214N2 214 North
## C172P2 C172P2 172 Point
## C185S2 C185S2 185 South
## C172P3 C172P3 172 Point
## C185N1 C185N1 185 North
## C172N2 C172N2 172 North
## C178S1 C178S1 178 South
## C185S1 C185S1 185 South
## C172S2 C172S2 172 South
## C178N2 C178N2 178 North
head(otu_table(biom))
## OTU Table: [6 taxa and 52 samples]
## taxa are rows
## C178N1 C178P1 C185P2 C206N2 C206P1 C206P2
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 1
## New.CleanUp.ReferenceOTU321320 0 0 0 0 0 0
## KC551585.1.1451 2 0 0 6 0 4
## JQ945994.1.1399 0 0 0 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 137 8 1 73 8 6
## C214P1 C214P2 C214P3 C214S1 C214S2 C214S3
## New.CleanUp.ReferenceOTU155901 0 0 1 0 1 0
## New.CleanUp.ReferenceOTU321320 0 0 0 0 0 0
## KC551585.1.1451 9 4 11 2 1 0
## JQ945994.1.1399 1 0 0 1 0 0
## EF653577.1.1339 0 0 0 4 0 0
## JQ814729.1.1495 11 4 27 40 8 23
## C185P1 C185P3 C199P3 C199S2 C199S3 C206N1
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 1 0 0 0 0
## KC551585.1.1451 3 2 5 0 0 11
## JQ945994.1.1399 0 0 0 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 6 4 2 11 2 56
## C178P2 C199N3 C206S1 C214N3 C199N2 C206N3
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 0 0 0 0 0
## KC551585.1.1451 0 1 4 3 0 3
## JQ945994.1.1399 0 0 2 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 10 23 6 19 5 849
## C206S2 C199N1 C199P1 C199S1 C214N1 C172P1
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 0 0 0 0 0
## KC551585.1.1451 1 0 8 2 5 0
## JQ945994.1.1399 0 0 0 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 14 24 5 5 3 2
## C199P2 C172N3 C172S3 C178S2 C178P3 C178S3
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 0 0 0 0 0
## KC551585.1.1451 2 5 8 0 1 0
## JQ945994.1.1399 0 0 0 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 2 121 31 0 0 5
## C172N1 C172S1 C178N3 C185N2 C185N3 C185S3
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 0 0 0 0 0
## KC551585.1.1451 0 0 1 0 0 0
## JQ945994.1.1399 0 0 0 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 5 16 4 6 27 4
## C214N2 C172P2 C185S2 C172P3 C185N1 C172N2
## New.CleanUp.ReferenceOTU155901 0 0 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 0 1 0 0 0
## KC551585.1.1451 3 2 0 2 1 6
## JQ945994.1.1399 0 0 0 0 0 0
## EF653577.1.1339 0 0 0 0 0 0
## JQ814729.1.1495 4 13 1 3 22 21
## C178S1 C185S1 C172S2 C178N2
## New.CleanUp.ReferenceOTU155901 0 0 0 0
## New.CleanUp.ReferenceOTU321320 0 0 0 0
## KC551585.1.1451 0 1 0 3
## JQ945994.1.1399 0 0 3 0
## EF653577.1.1339 0 0 0 0
## JQ814729.1.1495 2 3 21 18
# Custom plotting.
nolegend <- theme(legend.position="none")
readabund <- labs(y="read abundance")
# Normalize by relative abundance.
biom.relabund <- transform_sample_counts(biom, function(x) x / sum(x))
ordNMDS <- ordinate(biom.relabund, method="NMDS", distance="bray")
## Run 0 stress 0.1164511
## Run 1 stress 0.1164499
## ... New best solution
## ... Procrustes: rmse 0.0002640801 max resid 0.001355786
## ... Similar to previous best
## Run 2 stress 0.1164499
## ... Procrustes: rmse 4.59707e-06 max resid 2.549379e-05
## ... Similar to previous best
## Run 3 stress 0.1164499
## ... Procrustes: rmse 2.024418e-06 max resid 7.244113e-06
## ... Similar to previous best
## Run 4 stress 0.1164499
## ... New best solution
## ... Procrustes: rmse 4.512229e-07 max resid 1.37136e-06
## ... Similar to previous best
## Run 5 stress 0.1164511
## ... Procrustes: rmse 0.0002640564 max resid 0.001356755
## ... Similar to previous best
## Run 6 stress 0.1164511
## ... Procrustes: rmse 0.0002640336 max resid 0.001355855
## ... Similar to previous best
## Run 7 stress 0.1164511
## ... Procrustes: rmse 0.000264165 max resid 0.001352902
## ... Similar to previous best
## Run 8 stress 0.2490019
## Run 9 stress 0.1164499
## ... Procrustes: rmse 1.262105e-05 max resid 3.529697e-05
## ... Similar to previous best
## Run 10 stress 0.1164499
## ... Procrustes: rmse 3.669909e-06 max resid 1.715039e-05
## ... Similar to previous best
## Run 11 stress 0.1164499
## ... Procrustes: rmse 2.022824e-06 max resid 1.086017e-05
## ... Similar to previous best
## Run 12 stress 0.1164499
## ... Procrustes: rmse 8.11061e-07 max resid 2.358948e-06
## ... Similar to previous best
## Run 13 stress 0.1164499
## ... Procrustes: rmse 6.679075e-07 max resid 1.942404e-06
## ... Similar to previous best
## Run 14 stress 0.1164499
## ... Procrustes: rmse 2.77383e-06 max resid 1.271664e-05
## ... Similar to previous best
## Run 15 stress 0.1164511
## ... Procrustes: rmse 0.0002640178 max resid 0.001354858
## ... Similar to previous best
## Run 16 stress 0.1164511
## ... Procrustes: rmse 0.0002638534 max resid 0.00135605
## ... Similar to previous best
## Run 17 stress 0.1164499
## ... Procrustes: rmse 6.057614e-06 max resid 3.703111e-05
## ... Similar to previous best
## Run 18 stress 0.1164511
## ... Procrustes: rmse 0.0002642032 max resid 0.00135728
## ... Similar to previous best
## Run 19 stress 0.1164511
## ... Procrustes: rmse 0.0002639396 max resid 0.001359735
## ... Similar to previous best
## Run 20 stress 0.1164499
## ... Procrustes: rmse 2.275713e-06 max resid 9.427331e-06
## ... Similar to previous best
## *** Solution reached
ordNMDS.k3 <- ordinate(biom.relabund, method="NMDS", distance="bray", k=3)
## Run 0 stress 0.07869986
## Run 1 stress 0.07870007
## ... Procrustes: rmse 0.0002212905 max resid 0.0006546112
## ... Similar to previous best
## Run 2 stress 0.07869804
## ... New best solution
## ... Procrustes: rmse 0.0004127187 max resid 0.002047968
## ... Similar to previous best
## Run 3 stress 0.08345599
## Run 4 stress 0.07869827
## ... Procrustes: rmse 0.0002309752 max resid 0.001112312
## ... Similar to previous best
## Run 5 stress 0.08696054
## Run 6 stress 0.07869976
## ... Procrustes: rmse 0.0004640888 max resid 0.001623611
## ... Similar to previous best
## Run 7 stress 0.07890412
## ... Procrustes: rmse 0.00682703 max resid 0.03194959
## Run 8 stress 0.07869838
## ... Procrustes: rmse 0.0003182201 max resid 0.001253299
## ... Similar to previous best
## Run 9 stress 0.08337992
## Run 10 stress 0.08337912
## Run 11 stress 0.07899677
## ... Procrustes: rmse 0.008309483 max resid 0.03642162
## Run 12 stress 0.07870018
## ... Procrustes: rmse 0.0006233716 max resid 0.002629041
## ... Similar to previous best
## Run 13 stress 0.07875932
## ... Procrustes: rmse 0.003019153 max resid 0.01347439
## Run 14 stress 0.08337967
## Run 15 stress 0.07886131
## ... Procrustes: rmse 0.005660624 max resid 0.02758483
## Run 16 stress 0.08695834
## Run 17 stress 0.07869963
## ... Procrustes: rmse 0.0005062642 max resid 0.001462566
## ... Similar to previous best
## Run 18 stress 0.07870052
## ... Procrustes: rmse 0.0006595409 max resid 0.002172851
## ... Similar to previous best
## Run 19 stress 0.07869934
## ... Procrustes: rmse 0.0003273543 max resid 0.001333105
## ... Similar to previous best
## Run 20 stress 0.08695869
## *** Solution reached
ord.k3 <- plot_ordination(biom.relabund, ordNMDS.k3, shape="Site", color = "DateSite") + geom_point(size=2) + geom_polygon(aes(fill=DateSite), alpha=0.7) + labs(title = "Cladophora, 2014") + theme_bw()
ord.k3
# Facet by Date.
ord.k3.facet1 <- plot_ordination(biom.relabund, ordNMDS.k3, shape="Site", label = "Date") + geom_point(size=2.5) + facet_wrap(~Date) + labs(title = "Cladophora, 2014") + geom_polygon(aes(fill=DateSite)) + theme_bw()
ord.k3.facet1
# Facet by Site.
ord.k3.facet2 <- plot_ordination(biom.relabund, ordNMDS.k3, label = "Date") + geom_point(size=2.5) + facet_wrap(~Site, ncol = 1) + labs(title = "Cladophora, 2014") + geom_polygon(aes(fill=DateSite)) + theme_bw()
ord.k3.facet2
# ANOSIM...
# Remove singleton. (EDA)
biom.nosingle <- prune_taxa(taxa_sums(biom)>1, biom)
biom.nosingle # Same, so QIIME QC covered it.
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 51928 taxa and 52 samples ]
## sample_data() Sample Data: [ 52 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 51928 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 51928 tips and 51914 internal nodes ]
# Find methanotrophs
methanolist <- read.table(file = "~/Dropbox/clado-manuscript/clado_16S-archive/methanos.txt")
methanolist <- as.vector(methanolist$V1)
#
biom.relabund.methanos <- subset_taxa(biom.relabund, Genus %in% as.factor(methanolist))
biom.relabund.methanos
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 567 taxa and 52 samples ]
## sample_data() Sample Data: [ 52 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 567 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 567 tips and 566 internal nodes ]
head(tax_table(biom.relabund.methanos))
## Taxonomy Table: [6 taxa by 8 taxonomic ranks]:
## Kingdom Phylum
## AB240506.1.1496 "Bacteria" "Proteobacteria"
## KC432108.1.1352 "Bacteria" "Proteobacteria"
## New.CleanUp.ReferenceOTU247624 "Bacteria" "Proteobacteria"
## AJ422152.1.1406 "Bacteria" "Proteobacteria"
## AB989868.1.1453 "Bacteria" "Proteobacteria"
## AJ422161.1.1420 "Bacteria" "Proteobacteria"
## Class Order
## AB240506.1.1496 "Betaproteobacteria" "Burkholderiales"
## KC432108.1.1352 "Betaproteobacteria" "Burkholderiales"
## New.CleanUp.ReferenceOTU247624 "Betaproteobacteria" "Burkholderiales"
## AJ422152.1.1406 "Betaproteobacteria" "Burkholderiales"
## AB989868.1.1453 "Betaproteobacteria" "Burkholderiales"
## AJ422161.1.1420 "Betaproteobacteria" "Burkholderiales"
## Family Genus Species
## AB240506.1.1496 "Comamonadaceae" "Methylibium" NA
## KC432108.1.1352 "Comamonadaceae" "Methylibium" NA
## New.CleanUp.ReferenceOTU247624 "Comamonadaceae" "Methylibium" NA
## AJ422152.1.1406 "Comamonadaceae" "Methylibium" NA
## AB989868.1.1453 "Comamonadaceae" "Methylibium" NA
## AJ422161.1.1420 "Comamonadaceae" "Methylibium" NA
## Rank1
## AB240506.1.1496 NA
## KC432108.1.1352 NA
## New.CleanUp.ReferenceOTU247624 NA
## AJ422152.1.1406 NA
## AB989868.1.1453 NA
## AJ422161.1.1420 NA
#
relabund.methanos <- psmelt(biom.relabund.methanos)
relabund.methanos.genus <- relabund.methanos%>%
group_by(Sample, Genus)%>%
mutate(GenusAbundance = sum(Abundance))%>%
distinct(Sample, GenusAbundance, TreatmentGroup, Site, Date, Family, Genus)
head(relabund.methanos.genus)
## Source: local data frame [6 x 9]
## Groups: Sample, Genus [6]
##
## Sample GenusAbundance TreatmentGroup Site Date Family
## <chr> <dbl> <fctr> <fctr> <fctr> <fctr>
## 1 C178S2 1.369438 Early South 178 Crenotrichaceae
## 2 C199S1 1.369438 Late South 199 Crenotrichaceae
## 3 C185S1 1.369438 Early South 185 Crenotrichaceae
## 4 C199S3 1.369438 Late South 199 Crenotrichaceae
## 5 C185S2 1.369438 Early South 185 Crenotrichaceae
## 6 C178S1 1.369438 Early South 178 Crenotrichaceae
## # ... with 3 more variables: Genus <fctr>, Sample <chr>, Genus <fctr>
#
p <- ggplot(relabund.methanos.genus, aes(as.factor(Date), GenusAbundance, color = Site))
p <- p + geom_point() + facet_wrap(~Genus, scales="free_y")
p
# Means with error bars
# stats <- summarySE(biom, measurevar="Abundance", groupvars=c("bacsp","media")); stats
# p.stats <- ggplot(stats, aes(x = media, y = percloss, fill = bacsp))
# p.stats + geom_bar(stat = "identity", position=position_dodge(.9)) + geom_errorbar(aes(ymin=percloss-se, ymax=percloss+se), width=.2, colour="darkblue", position=position_dodge(.9)) + geom_rug() + scale_fill_grey()
# Plot richness.
biom.rich <- plot_richness(biom, x="Date", color="Site")
biom.rich
### Shannon linear model test.
#biom.rich[1][1]
# Stacked bar plots of methanotrophs.
barstack.methanos <- plot_bar(biom.relabund.methanos, x = "Date", fill="Site") + facet_wrap(~Genus, scales="free_y")
barstack.methanos
# Network plot.
network <- plot_net(biom, maxdist = 0.3, point_label = "SampleID.1", color = "Date", shape = "Site")
network
# Trees :)
biom.relabund.Methylococcaceae <- subset_taxa(biom.relabund, Family %in% "Methylococcaceae")
sample_data(biom.relabund.Methylococcaceae)$Date <- as.factor(sample_data(biom.relabund.Methylococcaceae)$Date)
biom.relabund.Methylococcaceae
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 79 taxa and 52 samples ]
## sample_data() Sample Data: [ 52 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 79 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 79 tips and 78 internal nodes ]
head(tax_table(biom.relabund.Methylococcaceae))
## Taxonomy Table: [6 taxa by 8 taxonomic ranks]:
## Kingdom Phylum
## New.CleanUp.ReferenceOTU16149 "Bacteria" "Proteobacteria"
## New.CleanUp.ReferenceOTU412315 "Bacteria" "Proteobacteria"
## New.CleanUp.ReferenceOTU12803 "Bacteria" "Proteobacteria"
## New.CleanUp.ReferenceOTU418181 "Bacteria" "Proteobacteria"
## New.CleanUp.ReferenceOTU42851 "Bacteria" "Proteobacteria"
## New.CleanUp.ReferenceOTU410894 "Bacteria" "Proteobacteria"
## Class Order
## New.CleanUp.ReferenceOTU16149 "Gammaproteobacteria" "Methylococcales"
## New.CleanUp.ReferenceOTU412315 "Gammaproteobacteria" "Methylococcales"
## New.CleanUp.ReferenceOTU12803 "Gammaproteobacteria" "Methylococcales"
## New.CleanUp.ReferenceOTU418181 "Gammaproteobacteria" "Methylococcales"
## New.CleanUp.ReferenceOTU42851 "Gammaproteobacteria" "Methylococcales"
## New.CleanUp.ReferenceOTU410894 "Gammaproteobacteria" "Methylococcales"
## Family Genus
## New.CleanUp.ReferenceOTU16149 "Methylococcaceae" "Methylomonas"
## New.CleanUp.ReferenceOTU412315 "Methylococcaceae" "Methylomicrobium"
## New.CleanUp.ReferenceOTU12803 "Methylococcaceae" "Methylomicrobium"
## New.CleanUp.ReferenceOTU418181 "Methylococcaceae" "Methylomonas"
## New.CleanUp.ReferenceOTU42851 "Methylococcaceae" "Methylomonas"
## New.CleanUp.ReferenceOTU410894 "Methylococcaceae" "Methylomonas"
## Species Rank1
## New.CleanUp.ReferenceOTU16149 NA NA
## New.CleanUp.ReferenceOTU412315 "buryatense" NA
## New.CleanUp.ReferenceOTU12803 NA NA
## New.CleanUp.ReferenceOTU418181 NA NA
## New.CleanUp.ReferenceOTU42851 NA NA
## New.CleanUp.ReferenceOTU410894 NA NA
tree1 <- plot_tree(biom.relabund.Methylococcaceae, color="Date", shape="Date", label.tips="Genus", size = "Abundance")
tree1
tree2 <- plot_tree(biom.relabund.Methylococcaceae, color="DateSite", label.tips = "Genus", size = "Abundance")
tree2
# Sandbox.